Many fields of human endeavor now use computing devices. Some of these fields collect and process vast amounts of data. As an example, medical data can grow exponentially. A medical facility may attach several sensors to an ailing or recovering patient, e.g., heart rate monitor, blood pressure monitor, electrocardiograph (EKG) monitor, blood content monitor, urine analysis monitor, brain activity monitor, various other electrodes, etc. When samples are taken from these sensors at a high frequency, the data storage requirements can become immense.
Some of this collected data can require many thousands of terabytes of data storage space, if not more. It is now commonplace for even home computer users to purchase hard disk drives (HDDs) for personal computing devices that provide a storage capacity of 1 terabyte or more. To reduce the amount of storage space that is needed to store data, various compression methods exist. Compression methods use a fewer number of bits to store data than the number of bits that represent the uncompressed data. Compressed data can thus require less storage space to store and reduced network bandwidth to transmit the compressed data as compared to the equivalent data prior to compression (“uncompressed data”).
Many compression methods, including compression methods used to compress medical data, are often selected without regard to the use or semantics of the underlying original data. For example, compression methods may be selected for some specified level of encoding to preserve values within a specified margin of error. The former may be termed lossless compression and the latter may be termed lossy compression. Lossless compression enables the compressed data to be expanded with full fidelity. However, this is done at the cost of storage space or network bandwidth. In contrast, lossy compression may need less space than lossless compression, but expanding the compressed data may not reproduce the original uncompressed data with as much fidelity as a lossless compression method.